2021 | Methodology of Mathematical and Computational Analysis II

2021　Methodology of Mathematical and Computational Analysis II

Font size S M L

Academic unit or major: Graduate major in Technology and Innovation Management

Instructor(s): Sasahara Kazutoshi

Class Format: Lecture / Exercise

Media-enhanced courses

Day/Period(Room No.): Sat3-4()

Group: -

Course number: TIM.A406

Credits: 1

Academic year: 2021

Offered quarter: 2Q

Syllabus updated: 2021/3/19

Lecture notes updated: -

Language used: Japanese

Access Index

Syllabus

Course description and aims

Students will learn data science to utilize vast and diverse data for business, and acquire applied skills in data analysis. In particular, we will lecture on the characteristics of unstructured data and their analysis methods, keeping in mind its application technology management, and acquire applied skills in data analysis through programming exercises.

Student learning outcomes

The goals of this course are as follows:
- To understand the basics of text analysis, network analysis, deep learning, and reinforcement learning
- To be able to apply these methods to unstructured data for the creation of new businesses

Keywords

Text, morphological analysis, sentiment analysis, social network analysis, neural networks, deep learning, reinforcement learning

Competencies that will be developed

✔ Specialist skills

Intercultural skills

Communication skills

Critical thinking skills

✔ Practical and/or problem-solving skills

Class flow

We will lecture on the basics of text analysis, network analysis, and deep learning for unstructured data (text, network, images, etc.), and through programming exercises, students will solidify their understanding and develop practical skills for data analysis (using Python and R). In addition, we will invite a corporate data scientist to lecture and to have a discussion on the cutting-edge applications of data science in business.

Course schedule/Required learning

	Course schedule	Required learning
Class 1	Network analysis	Understand the nature of network data, theories and methods for visualizing and analyzing networks
Class 2	Text analysis	Understand the nature of text data, principles and methods of text analysis, such as morphological analysis and sentiment analysis
Class 3	Programming exercise (1)	Acquire programming skills for text analysis and network analysis
Class 4	Deep learning	Understand the principles of deep learning and the methods for its application to unstructured data
Class 5	Reinforcement learning	Understand the principles of reinforcement learning and the methods for its application to unstructured data
Class 6	Programming exercise (2)	Acquire programming skills related to deep learning and reinforcement learning
Class 7	Guest lecture	Gain knowledge about cutting-edge data science applications in business

Out-of-Class Study Time (Preparation and Review)

After the lecture, it is recommended to read and review the relevant sections of the reference books.

Textbook(s)

Slides will be provided.

Reference books, course materials, etc.

Albert-Laszlo Barabasi, Network Science, Cambridge University Press (2016)

Assessment criteria and methods

Class contribution 20%, Exercise 40%, Report 40%

Related courses

TIM.B412 ： Strategic Management for Research and Development I
TIM.B413 ： Strategic Management for Research and Development II
TIM.A414 ： Introduction to Models and Experiments in Social Science
TIM.B535 ： Digital Marketing
TIM.A405 ： Methodology of Mathematical and Computational Analysis I

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

TOKYO INSTITUTE OF TECHNOLOGY